Initial commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- PandaReachDense-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: A2C
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: PandaReachDense-v2
+      type: PandaReachDense-v2
+    metrics:
+    - type: mean_reward
+      value: -3.82 +/- 0.62
+      name: mean_reward
+      verified: false
+---
+
+# **A2C** Agent playing **PandaReachDense-v2**
+This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

a2c-PandaReachDense-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7049410e49eb3560ea3d10ece2fab2e739f1577a4f21bb446c34bd4151cd5599
+size 108181

a2c-PandaReachDense-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.8.0

a2c-PandaReachDense-v2/data

@@ -0,0 +1,95 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=",
+        "__module__": "stable_baselines3.common.policies",
+        "__doc__": "\n    MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space (Tuple)\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Uses the CombinedExtractor\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ",
+        "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7e7859004ca0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7e7859011380>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {
+        ":type:": "<class 'dict'>",
+        ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=",
+        "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>",
+        "optimizer_kwargs": {
+            "alpha": 0.99,
+            "eps": 1e-05,
+            "weight_decay": 0
+        }
+    },
+    "num_timesteps": 1000000,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1690444422388824141,
+    "learning_rate": 0.0007,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[4.1848153e-01 4.0512256e-04 5.3332603e-01]\n [4.1848153e-01 4.0512256e-04 5.3332603e-01]\n [4.1848153e-01 4.0512256e-04 5.3332603e-01]\n [4.1848153e-01 4.0512256e-04 5.3332603e-01]]",
+        "desired_goal": "[[-1.3020096  -1.0341498  -0.8512943 ]\n [-1.4768023  -0.22002275 -0.40821344]\n [-1.2622032  -1.6953198  -0.7524524 ]\n [ 0.46247414 -0.3041589  -1.267795  ]]",
+        "observation": "[[ 4.1848153e-01  4.0512256e-04  5.3332603e-01  1.1530841e-03\n  -5.3850724e-04 -5.1857256e-03]\n [ 4.1848153e-01  4.0512256e-04  5.3332603e-01  1.1530841e-03\n  -5.3850724e-04 -5.1857256e-03]\n [ 4.1848153e-01  4.0512256e-04  5.3332603e-01  1.1530841e-03\n  -5.3850724e-04 -5.1857256e-03]\n [ 4.1848153e-01  4.0512256e-04  5.3332603e-01  1.1530841e-03\n  -5.3850724e-04 -5.1857256e-03]]"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": {
+        ":type:": "<class 'collections.OrderedDict'>",
+        ":serialized:": "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",
+        "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]",
+        "desired_goal": "[[ 0.06603388 -0.07170436  0.11388003]\n [-0.02128061  0.06319302  0.0318729 ]\n [-0.01164923  0.08104182  0.02657477]\n [ 0.06180524 -0.04356617  0.24164976]]",
+        "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"
+    },
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.0,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 50000,
+    "n_steps": 5,
+    "gamma": 0.99,
+    "gae_lambda": 1.0,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "normalize_advantage": false,
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.dict.Dict'>",
+        ":serialized:": "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",
+        "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])",
+        "_shape": null,
+        "dtype": null,
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "gAWVcwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLA4WUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWDAAAAAAAAAAAAIC/AACAvwAAgL+UaAtLA4WUjAFDlHSUUpSMBGhpZ2iUaBMolgwAAAAAAAAAAACAPwAAgD8AAIA/lGgLSwOFlGgWdJRSlIwNYm91bmRlZF9iZWxvd5RoEyiWAwAAAAAAAAABAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLA4WUaBZ0lFKUjA1ib3VuZGVkX2Fib3ZllGgTKJYDAAAAAAAAAAEBAZRoIksDhZRoFnSUUpSMCl9ucF9yYW5kb22UTnViLg==",
+        "dtype": "float32",
+        "_shape": [
+            3
+        ],
+        "low": "[-1. -1. -1.]",
+        "high": "[1. 1. 1.]",
+        "bounded_below": "[ True  True  True]",
+        "bounded_above": "[ True  True  True]",
+        "_np_random": null
+    },
+    "n_envs": 4
+}

a2c-PandaReachDense-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:17ecf253e405b034fab46e4fec9d722a3efa922b7a85bcd77be6ab46c1210ab4
+size 44734

a2c-PandaReachDense-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2940a9fd3b0fd69dc3e6c2218176351cd1a8c995602f6db5fa66bbca11a85759
+size 46014

a2c-PandaReachDense-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

a2c-PandaReachDense-v2/system_info.txt

@@ -0,0 +1,7 @@
+- OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.6
+- Stable-Baselines3: 1.8.0
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n    MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n    Used by A2C, PPO and the likes.\n\n    :param observation_space: Observation space (Tuple)\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param ortho_init: Whether to use or not orthogonal initialization\n    :param use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param full_std: Whether to use (n_features x n_actions) parameters\n        for the std instead of only (n_features,) when using gSDE\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n        a positive standard deviation (cf paper). It allows to keep variance\n        above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n    :param squash_output: Whether to squash the output using a tanh function,\n        this allows to ensure boundaries when using gSDE.\n    :param features_extractor_class: Uses the CombinedExtractor\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\n    :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ", "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7e7859004ca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7e7859011380>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVgQAAAAAAAAB9lCiMD29wdGltaXplcl9jbGFzc5SME3RvcmNoLm9wdGltLnJtc3Byb3CUjAdSTVNwcm9wlJOUjBBvcHRpbWl6ZXJfa3dhcmdzlH2UKIwFYWxwaGGURz/vrhR64UeujANlcHOURz7k+LWI42jxjAx3ZWlnaHRfZGVjYXmUSwB1dS4=", "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1690444422388824141, "learning_rate": 0.0007, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[4.1848153e-01 4.0512256e-04 5.3332603e-01]\n [4.1848153e-01 4.0512256e-04 5.3332603e-01]\n [4.1848153e-01 4.0512256e-04 5.3332603e-01]\n [4.1848153e-01 4.0512256e-04 5.3332603e-01]]", "desired_goal": "[[-1.3020096  -1.0341498  -0.8512943 ]\n [-1.4768023  -0.22002275 -0.40821344]\n [-1.2622032  -1.6953198  -0.7524524 ]\n [ 0.46247414 -0.3041589  -1.267795  ]]", "observation": "[[ 4.1848153e-01  4.0512256e-04  5.3332603e-01  1.1530841e-03\n  -5.3850724e-04 -5.1857256e-03]\n [ 4.1848153e-01  4.0512256e-04  5.3332603e-01  1.1530841e-03\n  -5.3850724e-04 -5.1857256e-03]\n [ 4.1848153e-01  4.0512256e-04  5.3332603e-01  1.1530841e-03\n  -5.3850724e-04 -5.1857256e-03]\n [ 4.1848153e-01  4.0512256e-04  5.3332603e-01  1.1530841e-03\n  -5.3850724e-04 -5.1857256e-03]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01]]", "desired_goal": "[[ 0.06603388 -0.07170436  0.11388003]\n [-0.02128061  0.06319302  0.0318729 ]\n [-0.01164923  0.08104182  0.02657477]\n [ 0.06180524 -0.04356617  0.24164976]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12  1.9740014e-01  0.0000000e+00\n  -0.0000000e+00  0.0000000e+00]]"}, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 50000, "n_steps": 5, "gamma": 0.99, "gae_lambda": 1.0, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "normalize_advantage": false, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True  True  True]", "bounded_above": "[ True  True  True]", "_np_random": null}, "n_envs": 4, "system_info": {"OS": "Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.6", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": -3.8163398226723073, "std_reward": 0.6239236257527516, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-27T08:41:22.642591"}

vec_normalize.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e525b904f55183ceda13158ff0e9e6f317348ef4c4c8f5c01bfb2f35b9b5dd67
+size 2387